Combining psychological models with machine learning to better predict people's decisions.
Creating agents that proficiently interact with people is critical for many applications. Towards creating these agents, models are needed that effectively predict people's decisions in a variety of problems. To date, two approaches have been suggested to generally describe people's decision behavio...
| Publicado en: | Synthese Vol. 189; pp. 81 - 94 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Springer Nature
Dec2012 Supplement
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=83223183&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 83223183 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: Dec2012 Supplement vid: 189 pid: 237 pub: Springer Nature artinfo: ui: 83223183 10.1007/s11229-012-0182-z ppf: 81 ppct: 13 formats: fmt: @attributes: type: P size: 163KB tig: atl: Combining psychological models with machine learning to better predict people's decisions. aug: au: Rosenfeld, Avi Zuckerman, Inon Azaria, Amos Kraus, Sarit affil: Department of Industrial Engineering, Jerusalem College of Technology, 91160 Jerusalem Israel Department of Industrial Engineering and Management, Ariel University Center of Samaria, 40700 Ariel Israel Department of Computer Science, Bar-Ilan University, 92500 Ramat-Gan Israel su: Machine learning Reason Algorithms Communication Prediction models Psychologists Economists Decision making sug: subj: Machine learning Reason Algorithms Communication Prediction models Psychologists Economists Decision making keyword: Mixed agent-human systems Psychological models for people's decisions ab: Creating agents that proficiently interact with people is critical for many applications. Towards creating these agents, models are needed that effectively predict people's decisions in a variety of problems. To date, two approaches have been suggested to generally describe people's decision behavior. One approach creates a-priori predictions about people's behavior, either based on theoretical rational behavior or based on psychological models, including bounded rationality. A second type of approach focuses on creating models based exclusively on observations of people's behavior. At the forefront of these types of methods are various machine learning algorithms.This paper explores how these two approaches can be compared and combined in different types of domains. In relatively simple domains, both psychological models and machine learning yield clear prediction models with nearly identical results. In more complex domains, the exact action predicted by psychological models is not even clear, and machine learning models are even less accurate. Nonetheless, we present a novel approach of creating hybrid methods that incorporate features from psychological models in conjunction with machine learning in order to create significantly improved models for predicting people's decisions. To demonstrate these claims, we present an overview of previous and new results, taken from representative domains ranging from a relatively simple optimization problem and complex domains such as negotiation and coordination without communication. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Synthese is a copyright of Springer, 2012. All Rights Reserved. item: Synthese holder: Springer Nature dt: @attributes: year: 2012 holdings: @attributes: islocal: N |
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